The increasing accumulation of Microplastics in the environment has created an urgent need for efficient and scalable removal technologies. While conventional methods such as Density Separation and Oil-based Extraction are effective for small-scale applications, they face limitations in terms of long processing times, the use of hazardous chemicals, and poor scalability. In this study, we present a Nanomaterial-assisted Magnetic Separation approach as a promising alternative for large-scale Microplastic Extraction. Hydrophobized Magnetite (Fe₃O₄) Nanoparticles, further coated with Silica (SiO₂), are employed to selectively capture microplastic particles dispersed in environmental samples. This method offers several key advantages, including rapid processing, high recovery efficiency, low environmental impact, selective extraction and potential for industrial automation. The performance of the system will be evaluated using Fluorescence Microscopy with dye-labeled microplastics, followed by automated Detection and Quantification through a pretrained YOLOV5 model via Machine Learning. This integrated approach highlights the potential of functionalized Magnetic Nanomaterials coupled with AI-based analysis for scalable and sustainable Microplastic remediation.